System and method for speech recognition-enabled automatic call routing

ABSTRACT

A system and method are disclosed for processing a call by receiving caller input in a speech format and utilizing phonemes to convert the speech input into word strings. The word strings are then converted into at least one object and at least one action. A synonym table is utilized to determine actions and objects. Objects generally represent nouns and adjective-noun combinations while actions generally represent verbs and adverb-verb combinations. The synonym table stores natural language phrases and their relationship with actions and objects. The actions and objects are utilized to determine a routing destination utilizing a routing table. The call is routed based on the routing table.

FIELD OF THE DISCLOSURE

The present disclosure relates generally to speech recognition and, moreparticularly, to speech recognition-enabled automatic call routingservice systems and methods.

BACKGROUND

Speech recognition systems are specialized computers that are configuredto process and recognize human speech and may also take action or carryout further processes. Developments in speech recognition technologiessupport “natural language” type interactions between automated systemsand users. A natural language interaction allows a person to speaknaturally. Voice recognition systems can react responsively to a spokenrequest. An application of natural language processing is speechrecognition with automatic call routing (ACR). A goal of an ACRapplication is to determine why a customer is calling a service centerand to route the customer to an appropriate agent or destination forservicing a customer request. Speech recognition technology generallyallows an ACR application to recognize natural language statements sothat the caller does not have to rely on a menu system. Natural languagesystems allow the customer to state the purpose of their call “in theirown words.”

In order for an ACR application to properly route calls, the ACR systemattempts to interpret the intent of the customer and selects a routingdestination. When a speech recognition system partially understands ormisunderstands the caller's intent, significant problems can result.Further, even in touch-tone ACR systems, the caller can depress thewrong button and have a call routed to a wrong location. When a calleris routed to an undesired system and realizes that there is a mistake,the caller often hangs up and retries the call. Another common problemoccurs when a caller gets “caught” or “trapped” in a menu that does notprovide an acceptable selection to exit the menu. Trapping a caller orrouting the caller to an undesired location leads to abandoned calls.Most call routing systems handle a huge volume of calls and, even if asmall percentage of calls are abandoned, the costs associated withabandoned calls are significant.

Current speech recognition systems, such as those sold by Speechworks™,operate utilizing a dynamic semantic model. The semantic modelrecognizes human speech and creates multiple word strings based onphonemes that the semantic model can recognize. The semantic modelassigns probabilities to each of the word strings using rules and othercriteria. However, the semantic model has extensive tables and businessrules, many that are “learned” by the speech recognition system. Thelearning portion of the system is difficult to set up and modify.Further, changing the word string tables in the semantic model can be aninefficient process. For example, when a call center moves or isassigned a different area code, the semantic system is retrained usingan iterative process.

Further, speech recognition systems are less than perfect for many otherreasons. Accordingly, there is a need for an improved automated methodand system of routing calls.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a simplified configuration of a telecommunicationsystem;

FIG. 2 is a general diagram that illustrates a method of routing calls;

FIG. 3 is a flow diagram that illustrates a method of processing androuting calls;

FIG. 4 is a table that depicts speech input and mapped synonym terms;and

FIG. 5 is a table illustrating action-object pairs and call destinationsrelating to the action object pairs.

DETAILED DESCRIPTION OF THE DRAWINGS

A system and method are disclosed for processing a call by receivingcaller input in a speech format and utilizing phonemes to convert thespeech input into word strings. The word strings are then converted intoat least one object and at least one action. A synonym table is utilizedto determine actions and objects. Objects generally represent nouns andadjective-noun combinations while actions generally represent verbs andadverb-verb combinations. The synonym table stores natural languagephrases and their relationship with actions and objects. The actions andobjects are utilized to determine a routing destination utilizing arouting table. The call is then routed based on the routing table.During the process, the word string, the actions, the objects and anaction-object pair can be assigned a probability value. The probabilityvalue represents a probability that the word string, the action, or theobject accurately represent the purpose or intent of the caller.

Referring now to FIG. 1, an illustrated communications system 100 thatincludes a call routing support system is shown. The communicationssystem 100 includes a speech enabled call routing system (SECRS) 118,such as an interactive voice response system having a speech recognitionmodule. The system 100 includes a plurality of potential calldestinations. Illustrative call destinations shown include servicedepartments, such as billing department 120, balance information 122,technical support 124, employee directory 126, and new customer servicedepartments 128. The communication network 116 receives calls from avariety of callers, such as the illustrated callers 110, 112, and 114.In a particular embodiment, the communication network 116 may be apublic telephone network or may be provided by a voice over Internetprotocol (VoIP) type network. The SECRS 118 may include components, suchas a processor 142, a synonym table 144, and an action-object routingmodule 140. The SECRS 118 is coupled to and may route calls to any ofthe destinations, as shown. In addition, the SECRS 118 may route callsto an agent, such as the illustrated live operator 130. An illustrativeembodiment of the SECRS 118 may be a call center having a plurality ofagent terminals attached (not shown). Thus, while only a single operator130 is shown, it should be understood that a plurality of differentagent terminals or types of terminals may be coupled to the SECRS 118,such that a variety of agents may service incoming calls. In addition,the SECRS 118 may be an automated call routing system. In a particularembodiment, the action-object routing module 140 includes anaction-object lookup table for matching action-object pairs to desiredcall routing destinations.

Referring to FIG. 2, an illustrative embodiment of an action-objectrouting module 140 is shown. In this particular embodiment, theaction-object routing module 140 includes an acoustic processing model210, semantic processing model 220, and action-object routing table 230.The acoustic model 210 receives speech input 202 and provides text asits output 204. Semantic model 220 receives text 204 from the acousticmodel 210 and produces an action-object pair 206 that is provided to theaction-object routing table 230. The routing table 230 receivesaction-object pairs 206 from semantic model 220 and produces a desiredcall routing destination 208. Based on the call routing destination 208,a call received at a call routing network 118 may be routed to a finaldestination, such as the billing department 120 or the technical supportservice destination 124 depicted in FIG. 1. In a particular embodiment,the action-object routing table 230 may be a look up table or aspreadsheet, such as Microsoft Excel™.

Referring to FIG. 3, an illustrative embodiment of a method ofprocessing a call using an automated call routing system is illustrated.The method starts at 300 and proceeds to step 302 where a speech inputsignal, such as a received utterance, is received or detected. Usingphonemes, the received speech input is converted into a plurality ofword strings or text in accordance with an acoustic model, as shown atsteps 304 and 306. In a particular embodiment, probability values areassigned to word strings based on established rules and the coherency ofthe word string. Next, at step 308, the word strings are parsed intoobjects and actions. Objects generally represent nouns andadjective-noun combinations while actions generally represent verbs andadverb-verb combinations. The actions and objects are assignedconfidence values or probability values based on how likely they are toreflect the intent of the caller. In a particular embodiment aprobability value or confidence level for the detected action and thedetected object is determined utilizing the priority value of the wordstring used to create the selected action and the selected object.

Many possible actions and objects may be detected or created form theword strings. The method attempts to determine and select a mostprobable action and object from a list of preferred objects and actions.To aid in this resolution a synonym table, such as the synonym table ofFIG. 4 can be utilized to convert detected actions and objects intopreferred actions and objects. Thus, detected objects and actions areconverted to preferred actions and objects and assigned a confidencelevel. The process of utilizing the synonym table can alter theconfidence level. The synonym table stores natural language phrases andtheir relationship with a set of actions and objects. Natural languagespoken by the caller can be compared to the natural language phrases inthe table. Using the synonym table, the system and method maps portionsof the natural phrases to detected objects and maps portions of thenatural spoken phrase to detected actions. Thus, the word strings areconverted into objects and actions, at steps 310 and 312 respectivelyand the selected action and object are set to the action and object thatwill be utilized to route the call. The action and object with thehighest confidence value are selected based on many criteria such asconfidence value, business rules etc in steps 310 and 312.

At step 310 and 312, multiple actions and objects can be detected andprovided with a probability value according to the likelihood that aparticular action or object identifies a customer's intent and thus willlead to a successful routing of the call and a dominant action anddominant object are determined. Next, at step 314, dominant objects andactions are paired together. At step 316, a paired action-object iscompared to an action-object routing table, such as the action objectrouting table of FIG. 5. The action-object routing table in FIG. 5 isgenerally a predetermined list. When objects and actions find a match,then the destination of the call can be selected at step 318, and thecall is routed, at step 320. The process ends at step 322.

Referring back to FIG. 4, as an example, it is beneficial to convertword strings such as “I want to have” to actions such as “get.” Thissubstantially reduces the size of the routing table. When a calldestination has a phone number change, a single entry in the routingtable may accommodate the change. Prior systems may require locatingnumerous entries in a voluminous database, or retraining a sophisticatedsystem. In accordance with the present system, dozens of differentlyexpressed or “differently spoken” inputs that have the same callerintent can be converted to a single detected action-object pair.Further, improper and informal sentences as well as slang can beconnected to an action-object pair that may not bear phoneticresemblance to the words uttered by the caller. With a directly mappedlookup table such as the table in FIG. 4, speech training and learningbehaviors found in conventional call routing systems are not required.The lookup table may be updated easily, leading to a low cost of systemmaintenance.

In addition, the method may include using a set of rules to convert aword string into an object or action. In a particular example,geographic designation information, such as an area code, may be used todistinguish between two potential selections or to modify theprobability value. In the event that the lookup table of theaction-object pair does not provide a suitable response, such as whereno entry is found in the routing table, the call may be routed to ahuman operator or agent terminal in response to a failed access to theaction-object lookup table.

Traditional automatic call routing systems are able to assign a correctdestination 50-80% of the time. Particular embodiments of the disclosedsystem and method using action-object tables can assign a correctdestination 85-95% of the time. Due to higher effective call placementrates, the number of abandoned calls (i.e., caller hang-ups prior tocompleting their task) is significantly reduced, thereby reducingoperating costs and enhancing customer satisfaction. In addition, theautomated call-routing system offers a speech recognition interface thatis preferred by many customers to touch tone systems.

The disclosed system and method offers significant improvements throughdecreased reliance on the conventional iterative semantic model trainingprocess. With the disclosed system, a semantic model assigns anaction-object pair leading to increased call routing accuracy andreduced costs. In particular implementations, the correct calldestination routing rate may reach the theoretical limit of 100%,depending upon particular circumstances. In some cases, certainaction-object systems have been implemented that achieve a 100% coveragerate, hit rate, and call destination accuracy rate.

The disclosed system and method is directed generally to integration ofaction-object technology with speech enabled automated call routingtechnology. The integration of these two technologies produces abeneficial combination as illustrated. The illustrated system has beendescribed in connection with a call center environment, but it should beunderstood that the disclosed system and method is applicable to otheruser interface modalities, such as web-based interfaces, touchtoneinterfaces, and other speech recognition type systems. The disclosedsystem and method provides for enhanced customer satisfaction becausethe customer's intent can be recognized by an action-object pair and ahigh percentage of calls reach the intended destination.

The above-disclosed subject matter is to be considered illustrative, andnot restrictive, and the appended claims are intended to cover all suchmodifications, enhancements, and other embodiments that fall within thetrue spirit and scope of the present invention. Thus, to the maximumextent allowed by law, the scope of the present invention is to bedetermined by the broadest permissible interpretation of the followingclaims and their equivalents, and shall not be restricted or limited bythe foregoing detailed description.

1. A method for processing a call comprising: transforming speech inputfrom a caller into a word sting; converting the word string into anobject and an action; determining a call destination based on the objectand the action; and routing the call to the call destination.
 2. Themethod of claim 1, further comprising using phonemes to convert thespeech input information to the word string.
 3. The method of claim 1,further comprising comparing text corresponding to the speech input to alist of word strings and assigning a probability to the word string. 4.The method of claim 1, further comprising parsing the word string intoan action and an object.
 5. The method of claim 1, further comprisingassigning a probability value to the word string wherein the probabilityvalue represents a probability that the word string matches an intent ofthe caller.
 6. The method of claim 1, further comprising assigning aprobability value to the object wherein the probability value representsa probability that the object represents an intent of the caller.
 7. Themethod of claim 1, further comprising assigning a probability value tothe action wherein the probability value represents a probability thatthe action represents an intent of the caller.
 8. The method of claim 1,wherein the action is one of a verb or an adverb-verb combination. 9.The method of claim 1, wherein the object is one of a noun or anadjective-noun combination.
 10. A system for routing calls comprising: acall routing system having a processor, the call routing systemconfigured to convert speech input from a caller into a word string, theprocessor configured to convert the word string into an object and anaction; an action-object rout nodule configured to determine adestination for the call based on the object and the action.
 11. Thesystem of claim 10, wherein the processor uses phonemes to convert thespeech input to the word string.
 12. The system of claim 10, wherein theprocessor assigns a probability to the word string.
 13. The system ofclaim 10, wherein the processor parses the word string into the actionand the object.
 14. The system of claim 10, wherein the processorassigns a probability value to the word string and the probability valuerepresents a probability that the word string represents an intent ofthe caller.
 15. The system of claim 10, wherein the processor assigns aprobability value to the object and the probability value represents aprobability that the object represents an intent of the caller.
 16. Thesystem of claim 10, wherein the processor assigns a probability value tothe action and the probability value represents a probability that theaction represents an intent of the caller.
 17. The system of claim 10,wherein the action is one of a verb or an adverb-verb combination. 18.The system of claim 10, wherein the object is one of a noun or anadjective-noun combination.
 19. The system of claim 10, wherein the callrouting system routes the call to a destination.
 20. A systemcomprising: an acoustic model configured to accept speech input and toproduce text at its output; a semantic model coupled to the acousticmodel for producing an action and an object responsive to the text; andan action-object routing table responsive to the semantic model toprovide a routing destination based on the action and the object. 21.The speech recognition system of claim 20, wherein more than 80% ofcalls processed are successfully routed to a desired destination.
 22. Amethod of call processing, the method comprising: transforming speechinput from a caller into one or more word strings; converting each ofthe one or more word strings into an object and an action; assigning afirst probability value to the object and a second probability value tothe action of each of the one or more word strings; and determining acall destination from the object and the action based on the first andthe second probability values.
 23. The method of claim 22, whereindetermining the call destination comprises: identifying a dominantaction and a dominant object of the one or more word strings based onthe assigned first and second probability values; and comparing thedominant action and the dominant object to predefined action-objectpairs in a routing table to identify the call destination.
 24. Themethod of claim 23, further comprising routing the call to theidentified call destination.